Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book.
The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.
Author(s): Akshara Makrariya, Brajesh Kumar Jha, Rabia Musheer, Anant Kant Shukla, Amrita Jha, Parvaiz Ahmad Naik
Series: River Publishers Series in Biomedical Engineering
Publisher: River Publishers
Year: 2023
Language: English
Pages: 324
City: Gistrup
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
List of Figures
List of Tables
List of Contributors
List of Abbreviations
Chapter 1: Modeling of Smoking Transmission Dynamics using Caputo–Fabrizio Type Fractional Derivative
1.1: Introduction
1.2: Basic Concept of Fractional Operators
1.3: Model Formulation
1.4: Caputo–Fabrizio Fractional Order Derivative
1.4.1: Stability Analysis of Model by Using Fixed-Point Theory
1.5: Numerical Results and Discussion
1.6: Conclusion
References
Chapter 2: Hybrid Feature Selection Techniques Utilizing Soft Computing Methods for Classifying Microarray Cancer Data
2.1: Introduction
2.2: Proposed Framework
2.2.1: Genes Extraction by ICA
2.2.2: Genetic Bee Colony (GBC) Algorithm
2.3: Used Classifier
2.3.1: Naive Bayes Classifier (NBC)
2.3.2: Support Vector Machine (SVM) Classifier
2.4: Experimental Setups
2.5: Experimental Result
2.6: Conclusion
References
Chapter 3: Finite Element Technique to Explicate Calcium Diffusion in Alzheimer’s Disease
3.1: Introduction
3.2: Literature Survey
3.3: Mathematical Formulations
3.3.1: Calcium Buffering
3.3.2: Voltage Gated Calcium Channel (VGCC)
3.3.3: Endoplasmic Reticulum (ER)
3.4: The Finite Element Technique
3.4.1: Approximated Geometry of the Cell
3.4.2: Physiological Boundary Conditions
3.4.3: Meshing of the domain
3.5: Results and Discussion
3.5.1: For Hippocampal Neuron
3.5.2: For Basal Forebrain Neuron
3.6: Conclusion
References
Chapter 4: Comparative Analysis of Computational Methods used in Protein–Protein Interaction (PPI) Studies
4.1: Introduction
4.1.1: Protein
4.1.2: Protein–Protein Interaction
4.1.2.1: Protein–protein interfacial characteristics
4.1.2.1.1: Size and shape
4.1.2.1.2: Complementarity between surfaces
4.1.2.1.3: Residue interface propensities
4.1.2.1.4: Hydrophobicity including Hydrogen bonding
4.1.2.1.5: Segmentation and secondary structure
4.1.2.1.6: Conformational changes on complex formation
4.1.2.2: PPI types
4.1.2.2.1: Homo oligomeric and Hetero oligomeric
4.1.2.2.2: Obligate and non obligate complexes
4.1.2.2.3: Transient and permanent complexes
4.1.2.2.4: Disordered to ordered complexes
4.1.2.3: PPI methods classification
4.2: In Silico Methods
4.2.1: Sequence Based Approaches
4.2.1.1: Ortholog based sequence approach
4.2.1.2: Domain pairs-based sequence approach
4.2.1.3: Statistical sequence-based approaches
4.2.1.3.1: Mirror tree method
4.2.1.3.2: PIPE
4.2.1.3.3: Co-evolutionary divergence
4.2.1.4: Machine learning sequence-based approaches
4.2.1.4.1: Auto-covariance
4.2.1.4.2: Pairwise similarity
4.2.1.4.3: Amino acid composition
4.2.1.4.4: Amino acid triad
4.2.1.4.5: UNISPPI
4.2.1.4.6: ETB viterbi
4.2.2: Structure Based Approaches
4.2.2.1: Template structure-based approaches
4.2.2.1.1: PRISM
4.2.2.1.2: PREPPI
4.2.2.2: Statistical structure-based approach
4.2.2.2.1: PID matrix score
4.2.2.2.2: Pre SPI
4.2.2.2.3: Domain cohesion and coupling
4.2.2.2.4: MEGADOCK
4.2.2.2.5: MetaApproach
4.2.2.3: Machine learning structure-based approaches
4.2.2.3.1: Random forest
4.2.2.3.2: Struct2Net
4.2.3: Gene Neighbourhood
4.2.4: Gene Fusion
4.2.5: In Silico Two-hybrid (I2h)
4.2.6: Phylogenetic Tree
4.2.7: Phylogenetic Profile
4.2.8: Gene Expression
4.3: PPI Networks and Databases
4.3.1: Creation of the PPI Networks
4.3.1.1: Choice of databases and data selection
4.3.1.2: Visualising PPI network
4.3.2: Different Databases
4.3.2.1: Interaction database
4.3.2.2: Metamining databases
4.3.2.3: Predictive interaction databases
4.3.2.4: Pathway database
4.3.2.5: Unifying database
4.4: Softwares Available for PPI
4.5: Conclusion
References
Chapter 5: Optimization of COVID-19 Risk Factors Using Fuzzy Logic Inference System
5.1: Introduction
5.2: Methodology
5.2.1: Proposed Mamdani Fuzzy Control System
5.2.2: Fuzzy Controller Design
5.2.3: Parameters Identification
5.2.4: Fuzzification
5.2.5: Fuzzy Inference Rule Base
5.2.6: Rule Evaluation By Fuzzy Inference Engine
5.2.7: Defuzzification
5.3: Results
5.4: Discussion
5.5: Conclusion and Future Work
References
Chapter 6: Dynamical Analysis of the Fractional-Order Mathematical Model of Hashimoto’s Thyroiditis
6.1: Introduction
6.2: Preliminaries
6.3: Formulation of Fractional-Order Model of Hashimoto’s Thyroiditis
6.4: Stability Analysis
6.5: Construction of a Numerical Solution Scheme
6.6: Numerical Segment
6.7: Conclusions
References
Chapter 7: Heated Laminar Vertical Jet of Pseudoplastic Fluids-Against Gravity
7.1: Introduction
7.2: Basic Equations
7.3: Results and Discussions
7.4: Graphical Presentation
7.5: Conclusion
References
Chapter 8: Analytical Solutions For Hydromagnetic Flow of Chemically Reacting Williamson Fluid Over a Vertical Cone and Wedge with Heat Source/Sink
8.1: Introduction
8.2: Formulation of the Problem
8.3: Solution of the Problem
8.4: Results and Discussion
8.5: Conclusion
References
Chapter 9: Aboodh Transform Homotopy Perturbation Method for Solving Newell-Whitehead-Segel Equation
9.1: Introduction
9.2: Basic Definition of Aboodh Transform
9.2.1: Some Properies of Aboodh Transform
9.3: Idea of Aboodh Transform Homotopy Perturbation Method
9.4: Some Illustrations
9.5: Conclusion
References
Chapter 10: Transmission and Control of Droplet Infection from Exotic to Native Population: A Mathematical Model
10.1: Introduction
10.2: Basic Assumptions and Formulation of the Model
10.3: Disease Free Equilibrium Point and Basic Reproduction Number
10.4: Stability Around the Disease Free Equilibrium Point
10.4.1: Linear Stability Around the Disease Free Equilibrium Point E0
10.4.2: Non Linear Stability Around the Disease Free Equilibrium Point E0
10.5: Existence of Endemic Equilibrium Point
10.6: Stability Around the Endemic Equilibrium Point
10.6.1: Linear Stability Around the Endemic Equilibrium Point E1
10.6.2: Non Linear Stability Around the Endemic Equilibrium Point E1
10.7: Sensitivity Analysis of Basic Reproduction Number and State Variables
10.8: Optimal Control Problem
10.9: Numerical Simulation
10.10: Conclusions
References
Chapter 11: A Fractional Calculus Model to Depict the Calcium Diffusion for Neurodegenerative Disease
11.1: Introduction
11.2: Mathematical Preliminaries
11.3: Mathematical Modelling of Advection Diffusion Equation
11.3.1: A Fractional Calculus Model of Advection Diffusion Equation
11.4: Results and Discussion
11.5: Conclusion
References
Chapter 12: Approximate Analytic Solution for Tumour Growth and Human Head Heat Distribution Singular Boundary Value Model by High-Resolution Order-Preserving Fuzzy Transform
12.1: Introduction
12.2: Preliminaries of Fuzzy Transform
12.3: Exponential Basis Approximated Fuzzy Components
12.4: Order Preserving Fuzzy Component Scheme
12.5: Tumor Growth and Oxygen Diffusion in a Spherical Cell Model
12.6: Heat Distribution in the Human Head
12.7: Numerical Simulations and Performance Evaluation
12.8: Conclusion and Remarks
References
Chapter 13: Analysis of One-Dimensional Groundwater Recharge by Spreading using Hybrid Differential Transform and Finite Difference Method
13.1: Introduction
13.2: Research Gap
13.3: Mathematical Formulation
13.4: Methodology
13.5: Hybrid Differential Transform and Finite Difference Method
13.6: Solution
13.7: Results and Discussion
13.8: Conclusions
13.9: Utilities of Research
References
Chapter 14: Numerical Solution of Physiological Thermoregulatory Disturbances in Cold Environment
14.1: Introduction
14.2: Material and Methods
14.3: Result
14.4: Discussion
References
Chapter 15: Mathematical Modelling of Transient Heat Conduction in Biological System by Finite Element Method and Coding in MATLAB
15.1: Introduction
15.2: General Procedure of Finite Element Method
15.3: Process of Finite Element Method
15.3.1: Definition of the Problem and its Domain: One Dimensional Thermal Equation of Biological System
15.4: Steps Involved in Finite Element Process
15.5: Model-2: One Dimensional Quadratic Interpolation Model
15.5.1: Assumption of a Suitable form of Variation in T for Quadratic Element
15.6: Assembly of Elements
15.7: Matrix Form of Element Equation
15.8: Algorithm and Computer Program to Solve Heat Equation using FEM in Matlab:
15.9: Result and Discussion
References
Index
About the Editors
